On the Validity of Covariate Adjustment for Estimating Causal Effects
This work addresses a fundamental challenge in causal inference for researchers and practitioners, offering a theoretical advancement that is incremental but rigorous.
The paper tackles the problem of identifying causal effects from observational data in the presence of confounders by providing a complete graphical criterion for covariate adjustment, termed the adjustment criterion, and derives corollaries from its completeness.
Identifying effects of actions (treatments) on outcome variables from observational data and causal assumptions is a fundamental problem in causal inference. This identification is made difficult by the presence of confounders which can be related to both treatment and outcome variables. Confounders are often handled, both in theory and in practice, by adjusting for covariates, in other words considering outcomes conditioned on treatment and covariate values, weighed by probability of observing those covariate values. In this paper, we give a complete graphical criterion for covariate adjustment, which we term the adjustment criterion, and derive some interesting corollaries of the completeness of this criterion.